2016
Feng, T.; Timmermans, H. J. P.
Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data Journal Article
In: Transportation Planning and Technology, vol. 39, iss. 2, 2016, ISSN: 10290354.
Abstract | Links | BibTeX | Tags: activity-travel data, Bayesian network, classification algorithm, data imputation, decision tree, Global Positioning System (GPS), rules, travel survey
@article{Feng2016,
title = {Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data},
author = {T. Feng and H. J. P. Timmermans},
doi = {10.1080/03081060.2015.1127540},
issn = {10290354},
year = {2016},
date = {2016-01-01},
journal = {Transportation Planning and Technology},
volume = {39},
issue = {2},
abstract = {Global Positioning System (GPS) technologies have been increasingly considered as an alternative to traditional travel survey methods to collect activity-travel data. Algorithms applied to extract activity-travel patterns vary from informal ad-hoc decision rules to advanced machine learning methods and have different accuracy. This paper systematically compares the relative performance of different algorithms for the detection of transportation modes and activity episodes. In particular, naive Bayesian, Bayesian network, logistic regression, multilayer perceptron, support vector machine, decision table, and C4.5 algorithms are selected and compared for the same data according to their overall error rates and hit ratios. Results show that the Bayesian network has a better performance than the other algorithms in terms of the percentage correctly identified instances and Kappa values for both the training data and test data, in the sense that the Bayesian network is relatively efficient and generalizable in the context of GPS data imputation.},
keywords = {activity-travel data, Bayesian network, classification algorithm, data imputation, decision tree, Global Positioning System (GPS), rules, travel survey},
pubstate = {published},
tppubtype = {article}
}
Global Positioning System (GPS) technologies have been increasingly considered as an alternative to traditional travel survey methods to collect activity-travel data. Algorithms applied to extract activity-travel patterns vary from informal ad-hoc decision rules to advanced machine learning methods and have different accuracy. This paper systematically compares the relative performance of different algorithms for the detection of transportation modes and activity episodes. In particular, naive Bayesian, Bayesian network, logistic regression, multilayer perceptron, support vector machine, decision table, and C4.5 algorithms are selected and compared for the same data according to their overall error rates and hit ratios. Results show that the Bayesian network has a better performance than the other algorithms in terms of the percentage correctly identified instances and Kappa values for both the training data and test data, in the sense that the Bayesian network is relatively efficient and generalizable in the context of GPS data imputation.